I lead the Oxford Protein Informatics group, a research group of over 20 people working on diverse problems across protein structure, immunoinformatics, biological networks and small molecule drug discovery.

Positions and Employment

  • Professor, Department of Statistics, University of Oxford (2008-)
  • Deputy Executive Chair of the Engineering and Physical Sciences Research Council (2019-2021)
  • Head of Department of Statistics (2015-2019)
  • Deputy Head of the Mathematical Physical and Life Sciences (MPLS) Division (2018-2020)
  • Associate Head (Research) of MPLS Division (2014-2019)
  • Associate Head (Impact and Innovation) of MPLS Division (2013–2014)
  • Director - Systems Approaches to Biomedical Research Centre for Doctoral Training (2009-)
  • Director - Systems Biology Doctoral Training Centre (2007–2009)
  • University Lecturer, Department of Statistics, University of Oxford (2002-2008)
  • Welcome Trust Research Fellow, University of California Los Angeles (2000 - 2002)

Academic Qualifications

  • PhD - Jesus College, University of Cambridge
  • MA - Chemistry, University College, University of Oxford

Selected conference presentations (since 2014)

EBI Biologics (2014), SMBE (2014), Drug Discovery Summit (2014), Networks in biological sciences (2015), Gordon Conference Membrane Protein folding (2105), 3DSIG (2015), CCP4 (2016), PPS (2016), Americas Antibody Conference (2016), Gordon Conference CADD (2017), ISMB (2017), IEEE Computational Intelligence in Bioinformatics and Computational Biology (2017), ISMB (2018), Keynote RosettaCon (2018), UK QSAR (2019), PEGS America (2019), Gordon Conference Proteins (2019), PEGS Europe (2019), Cambridge networks day (2019), CCPBiosim (2019), ELRIG (2019)


  • Video: Exciting Scientists - interview with Winton Capital [link]
  • Video: The structural antibody database and its importance ('LabTube meets' series) [link]
  • Video: The developability issues affecting the efficacy of therapeutic monoclonal antibodies ('LabTube meets' series) [link]
  • Video: Predicting Loop Conformational Ensembles (ISMB 2018) [link]
  • Video: Structurally mapping next generation sequencing repertoires of antibodies to aid in-silico therapeutic design (ELRIG Drug Discovery 2018) [link]
  • Podcast: Why aren't we dead? (Oxford Sparks) [link]
  • Podcast: Tech Tent on the BBC World Service (Interview on the role of AI in drug discovery) [link]
  • Video: Oxford University Scientific Society (Lecture: The Protein Structure Universe:from Creation to Medicine) [link]
  • Video: Digital Futures - Royal Society of Chemistry [link]

Research Interests

My research covers several areas in protein structure prediction and protein interaction networks, combining both theoretical work and empirical analyses. We work on developing novel methodologies to understand and predict protein evolution, interaction, structure and function. Our work is focussed in the four main areas listed below:

  • Protein structure
  • Immunoinformatics
  • Biological Networks
  • Small molecules
For more information about the research and members of the Oxford Protein Informatics Group, see our group web pages. A list of all software developed in my group can also be found here.


Carbery, A., Skyner, R.E., von Delft, F. & Deane, C.M. (Rxiv) Fragment libraries designed to be functionally diverse recover protein binding information more efficiently than standard structurally diverse libraries bioRxiv
Chinery, L., Wahome, N., Moal, I.H. & Deane, C.M. (Rxiv) Paragraph - Antibody paratope prediction using Graph Neural Networks with minimal feature vectors bioRxiv
Moesser, M.A., Klein, D., Boyles, F., Deane, C.M., Baxter, A. & Morris, G.M. (Rxiv) Protein-Ligand Interaction Graphs: Learning from Ligand-Shaped 3D Interaction Graphs to Improve Binding Affinity Prediction bioRxiv
Sanchez-Garcia, R., Havasi, D., Takács, G., Robinson, M.C., Lee, A., von Delft, F. & Deane, C.M. (Rxiv) CoPriNet: Deep learning compound price prediction for use in de novo molecule generation and prioritization ChemRxiv
Pardo-Diaz, J., Poole, P., Beguerisse-Diaz, M., Deane, C.M. & Reinert, G. (Rxiv) Generating weighted and thresholded gene coexpression networks using signed distance correlation bioRxiv
Hadfield, T.E., Imrie, F., Merritt, A., Birchall, K. & Deane, C.M. (Rxiv) Incorporating Target-Specific Pharmacophoric Information Into Deep Generative Models For Fragment Elaboration bioRxiv
Pardo-diaz, J., Beguerisse-diaz, M., Poole, P.S., Deane, C.M. & Reinert, G. (2022) Extracting Information from Gene Coexpression Networks of Rhizobium leguminosarum Journal of Computational Biology, ():
Wang, Y., Tsitsiklis, A., Gao, W., Chu, H.H., Zhang, Y., Li, W., Wong, W.K., Deane, C.M., Neau, D., Slansky, J.E., Thomas, P.G., Robey, E.A. & Dai, S. (2022) Novel Vβ specific germline contacts shape an elite controller T cell response Frontiers in Immunology (Accepted, link to preprint)
Schneider, C., Raybould, M.I.J. & Deane, C.M. (2022) SAbDab in the age of biotherapeutics: updates including SAbDab-nano, the nanobody structure tracker Nucleic Acids Research, 50(D1):D1368-D1372
Crook, O.M., Chung, C.w. & Deane, C.M. (2022) Empirical Bayes functional models for hydrogen deuterium exchange mass spectrometry Communications Biology, 5:588
Wilsenach, J.B., Warnaby, C.E., Deane, C.M. & Reinert, G.D. (2022) Ranking of communities in multiplex spatiotemporal models of brain dynamics Appl Netw Sci, 7(1):15
Crook, O.M., Chung, C.W. & Deane, C.M. (2022) Challenges and Opportunities for Bayesian Statistics in Proteomics Journal of proteome research, 21(4):849-864
Outeiral, C., Nissley, D.A. & Deane, C.M. (2022) Current structure predictors are not learning the physics of protein folding Bioinformatics, 38(7):1881-1887
Hummer, A.M., Abanades, B. & Deane, C.M. (2022) Advances in computational structure-based antibody design Current Opinion in Structural Biology, 74:102379
Lomize, A.L., Schnitzer, K.A., Todd, S.C., Cherepanov, S., Outeiral, C., Deane, C.M. & Pogozheva, I.D. (2022) Membranome 3.0: Database of single-pass membrane proteins with AlphaFold models Protein Science, 31(5):e4318
Hadfield, T.E. & Deane, C.M. (2022) AI in 3D compound design Current Opinion in Structural Biology, 73:102326
Abanades, B., Georges, G., Bujotzek, A. & Deane, C.M. (2022) ABlooper: Fast accurate antibody CDR loop structure prediction with accuracy estimation Bioinformatics, ():btac016
Raybould, M.I.J., Rees, A.R. & Deane, C.M. (2021) Current strategies for detecting functional convergence across B-cell receptor repertoires MAbs, 13(1):1996732
Outeiral, C., Morris, G.M., Shi, J., Strahm, M., Benjamin, S.C. & Deane, C.M. (2021) Investigating the potential for a limited quantum speedup on protein lattice problems New Journal of Physics, 23(10):103030
Macpherson, A., Laabei, M.L., Ahdash, Z.A., Graewert, M., Birtley, J.R., Schulze, S., Crennell, S., Robinson, S.A., Holmes, B., Oleinikovas, V., Nilsson, P.H., Snowden, J., Ellis, V., Mollnes, T.E., Deane, C.M., Svergun, D., Lawson, A.D.G. & van den Elsen, J. (2021) The allosteric modulation of Complement C5 by knob domain peptides eLife, 10:e63586
Imrie, F., Bradley, A.R. & Deane, C. (2021) Generating Property-Matched Decoy Molecules Using Deep Learning Bioinformatics, 37(15):2134-2141
Nissley, D.A., Carbery, A., Chonofsky, M. & Deane, C.M. (2021) Ribosome occupancy profiles are conserved between structurally and evolutionarily related yeast domains Bioinformatics, 37(13):1853-1859
Pardo-Diaz, J., Bozhilova, L.V., Mariano, B.D., Poole, P.S., Deane, C.M. & Reinert, G. (2021) Robust gene coexpression networks using signed distance correlation Bioinformatics, 37(14):1982-1989
Richardson, E., Galson, J.D., Kellam, P., Kelly, D.F., Smith, S.E., Palser, A., Watson, S. & Deane, C.M. (2021) A computational method for immune repertoire mining that identifies novel binders from different clonotypes, demonstrated by identifying anti-Pertussis toxoid antibodies MAbs, 13(1):1869406
Raybould, M.I.J., Kovaltsuk, A., Marks, C. & Deane, C.M. (2021) CoV-AbDab: the Coronavirus Antibody Database Bioinformatics, 37(5):734-735
Klimm, F., Deane, C.M. & Reinert, G. (2021) Hypergraphs for predicting essential genes using multiprotein complex data Journal of Complex Networks, 9(2):cnaa028
Wong, W.K., Robinson, S.A., Bujotzek, A., Georges, G., Lewis, A.P., Shi, J., Snowden, J., Taddese, B. & Deane, C.M. (2021) Ab-Ligity: Identifying sequence-dissimilar antibodies that bind to the same epitope MAbs, 13(1):1873478
Raybould, M.I.J., Marks, C., Kovaltsuk, A., Lewis, A.P., Shi, J. & Deane, C.M. (2021) Public Baseline and Shared Response Structures Support the Theory of Antibody Repertoire Functional Commonality PLoS Computational Biology, 17(3):e1008781
Imrie, F., Hadfield, T.E., Bradley, A.R. & Deane, C.M. (2021) Deep generative design with 3D pharmacophoric constraints Chemical Science, 12:14577-14589
Olsen, T.H., Boyles, F. & Deane, C.M. (2021) OAS: A diverse database of cleaned, annotated and translated unpaired and paired antibody sequences Protein Science
Schneider, C., Buchanan, A., Taddese, B. & Deane, C.M. (2021) DLAB—Deep learning methods for structure-based virtual screening of antibodies Bioinformatics, 38(2):377-383
Boyles, F., Deane, C.M. & Morris, G.M. (2021) Learning from Docked Ligands: Ligand-Based Features Rescue Structure-Based Scoring Functions When Trained on Docked Poses Journal of Chemical Information and Modeling
Schwarz, D., Georges, G., Kelm, S., Shi, J., Vangone, A. & Deane, C.M. (2021) Co-evolutionary distance predictions contain flexibility information Bioinformatics, 38(1):65-72
Ghraichy, M., von Niederhäusern, V., Kovaltsuk, A., Galson, J.D., Deane, C.M. & Trück, J. (2021) Different B cell subpopulations show distinct patterns in their IgH repertoire metrics eLife, 10:e73111
Marks, C., Hummer, A.M., Chin, M. & Deane, C.M. (2021) Humanization of antibodies using a machine learning approach on large-scale repertoire data Bioinformatics, 37(22):4041-4047
Robinson, S.A., Raybould, M.I.J., Schneider, C., Wong, W.K., Marks, C. & Deane, C.M. (2021) Epitope profiling using computational structural modelling demonstrated on coronavirus-binding antibodies PLoS Computational Biology, 17(2):e1009675
Bozhilova, L.V., Pardo-Diaz, J., Reinert, G. & Deane, C.M. (2020) COGENT: evaluating the consistency of gene co-expression networks Bioinformatics, ():btaa787
Galson, J.D., Schaetzle, S., Bashford-Rogers, R.J.M., Raybould, M.I.J., Kovaltsuk, A., Kilpatrick, G.J., Minter, R., Finch, D.K., Dias, J., James, L., Thomas, G., Lee, W.Y.J., Betley, J., Cavlan, O., Leech, A., Deane, C.M., Seoane, J., Caldas, C., Pennington, D., Pfeffer, P. & Osbourn, J. (2020) Deep sequencing of B cell receptor repertoires from COVID-19 patients reveals strong convergent immune signatures Frontiers in Immunology, 11:605170
Ghraichy, M., Galson, J.D., Kovaltsuk, A., von Niederhäusern, V., Schmid, J.M., Miho, E., Kelly, D.F., Deane, C.M. & Trück, J. (2020) Maturation of Naïve and Antigen-experienced B-cell Receptor Repertoires with Age Frontiers in Immunology, 11:1734
Outeiral, C., Strahm, M., Shi, J., Morris, G.M., Benjamin, S.C. & Deane, C.M. (2020) The prospects of quantum computing in computational molecular biology WIRES, 11(1):e1481
Marks, C. & Deane, C.M. (2020) How repertoire data is changing antibody science Journal of Biological Chemistry, 295:9823-9837
Wong, W.K., Marks, C., Leem, J., Lewis, A.P., Shi, J. & Deane, C.M. (2020) TCRBuilder: Multi-state T-cell receptor structure prediction Bioinformatics, 36(11):3580-3581
Scantlebury, J., Brown, N., Von Delft, F. & Deane, C.M. (2020) Dataset Augmentation Allows Deep Learning-Based Virtual Screening To Better Generalise To Unseen Target Classes, And Highlight Important Binding Interactions. Journal of Chemical Information Modeling, 60(8):3722-3730
Imrie, F., Bradley, A.R., van der Schaar, M. & Deane, C.M. (2020) Deep Generative Models for 3D Linker Design Journal of Chemical Information Modeling, 60(4):1983-1995
Kovaltsuk, A., Raybould, M.I.J., Wong, W.K., Marks, C., Kelm, S., Snowden, J., Trück, J. & Deane, C.M. (2020) Structural Diversity of B-cell Receptor Repertoires along the B-cell Differentiation Axis in Humans and Mice PLoS Computational Biology, 16(2):e1007636
Raybould, M.I.J., Marks, C., Lewis, A.P., Shi, J., Bujotzek, A., Taddese, B. & Deane, C.M. (2020) Thera-SAbDab: the Therapeutic Structural Antibody Database Nucleic Acids Research, 48(D1):D383-D388
Klimm, F., Toledo, E.M., Monfeuga, T., Zhang, F., Deane, C.M. & Reinert, G. (2020) Functional module detection through integration of single-cell RNA sequencing data with protein-protein interaction networks. BMC Bioinformatics, 21:756
Knapp, B., van der Merwe, P.A., Dushek, O. & Deane, C.M. (2019) MHC binding affects the dynamics of different T-cell receptors in different ways PLoS Computational Biology, 15:1-17
Wong, W.K., Leem, J. & Deane, C.M. (2019) Comparative analysis of the CDR loops of antigen receptors Frontiers in Immunology, 10:2454
Ebejer, J.P., Finn, P.W., Wong, W.K., Deane, C.M. & Morris, G.M. (2019) Ligity: A Non-Superpositional, Knowledge-Based Approach to Virtual Screening Journal of Chemical Information and Modeling, 59(6):2600-2616
Raybould, M.I.J., Marks, C., Krawczyk, K., Taddese, B., Nowak, J., Lewis, A.P., Bujotzek, A., Shi, J. & Deane, C.M. (2019) Five Computational Developability Guidelines for Therapeutic Antibody Profiling Proceedings of the National Academy of Sciences USA, 116(10):4025-4030
Chonofsky, M., de Oliveira, S.H.P., Krawczyk, K. & Deane, C.M. (2019) The evolution of contact prediction: Evidence that contact selection in statistical contact prediction is changing Bioinformatics, ():btz816
West, C.E., de Oliveira, S.H.P. & Deane, C.M. (2019) RFQAmodel: Random Forest Quality Assessment to identify a predicted protein structure in the correct fold PLoS One, 14(10):1-16
Bozhilova, L.V., Whitmore, A.V., Wray, J., Reinert, G. & Deane, C.M. (2019) Measuring rank robustness in scored protein interaction networks BMC Bioinformatics, 20:446
Boyles, F., Deane, C.M. & Morris, G.M. (2019) Learning From The Ligand: Using Ligand-Based Features To Improve Binding Affinity Prediction Bioinformatics, 36(3):758-764
Schwarz, D., Merget, B., Deane, C.M. & Fulle, S. (2019) Modeling conformational flexibility of kinases in inactive states Proteins, 87(11):943-951
Krawczyk, K., Raybould, M.I.J., Kovaltsuk, A. & Deane, C.M. (2019) Looking for Therapeutic Antibodies in Next Generation Sequencing Repositories MAbs, 11(7):1197-1205
Raybould, M.I.J., Wong, W.K. & Deane, C.M. (2019) Antibody-antigen Complex Modelling in the Era of Immunoglobulin Repertoire Sequencing Molecular Systems Design & Engineering, 4:679-688
Demharter, S., Knapp, B., Deane, C.M. & Minary, P. (2019) HLA-DM stabilises the empty MHCII binding groove: A model using customised Natural Move Monte Carlo Journal of Chemical Information and Modeling, 59(6):2894-2899
Marks, C. & Deane, C.M. (2018) Increasing the accuracy of protein loop structure prediction with evolutionary constraints Bioinformatics, ():bty996
Kovaltsuk, A., Krawczyk, K., Kelm, S., Snowden, J. & Deane, C.M. (2018) Filtering Next-Generation Sequencing of the Ig Gene Repertoire Data Using Antibody Structural Information Journal of Immunology, 201(12):3694-3704
Leem, J., Georges, G., Shi, J. & Deane, C.M. (2018) Antibody side chain conformations are position-dependent Proteins: Structure, Function, and Bioinformatics, 86(4):383-392
Knapp, B., Alcala, M., Zhang, H., West, C.E., van der Merwe, P.A. & Deane, C.M. (2018) pyHVis3D: Visualising Molecular Simulation deduced H-bond networks in 3D: Application to T-cell receptor interactions Bioinformatics, ():btx842
Wegner, A.E., Ospina-Forero, L., Gaunt, R.E., Deane, C.M. & Reinert, G. (2018) Identifying networks with common organizational principles Journal of Complex Networks, ():cny003
Marks, C., Shi, J. & Deane, C.M. (2018) Predicting loop conformational ensembles Bioinformatics, 34(6):949-956
Knapp, B., Ospina, L. & Deane, C.M. (2018) Avoiding false positive conclusions in molecular simulation: the importance of replicas Journal of Chemical Theory and Computation, 14(12):6127-6138
Wong, W.K., Georges, G., Ros, F., Kelm, S., Lewis, A.P., Taddese, B., Leem, J. & Deane, C.M. (2018) SCALOP: sequence-based antibody canonical loop structure annotation Bioinformatics, 35(10):1774-1776
Imrie, F., Bradley, A.R., van der Schaar, M. & Deane, C.M. (2018) Protein Family-specific Models using Deep Neural Networks and Transfer Learning Improve Virtual Screening and Highlight the Need for More Data Journal of Chemical Information and Modeling, 58(11):2319-2330
Ospina-Forero, L., Deane, C.M. & Reinert, G. (2018) Assessment of model fit via network comparison methods based on subgraph counts Journal of Complex Networks, ():cny017
Kovaltsuk, A., Leem, J., Kelm, S., Snowden, J., Deane, C.M. & Krawczyk, K. (2018) Observed Antibody Space: a resource for data mining next generation sequencing of antibody repertoires Journal of Immunology, 201(7):2502-2509
Krawczyk, K., Kelm, S., Kovaltsuk, A., Galson, J.D., Kelly, D., Trück, J., Regep, C., Leem, J., Wong, W.K., Nowak, J., Snowden, J., Wright, M., Starkie, L., Scott-Turner, A., Shi, J. & Deane, C.M. (2018) Structurally Mapping Antibody Repertoires Frontiers in Immunology, 9:1698
de Oliveira, S.H.P. & Deane, C.M. (2018) Combining co-evolution and secondary structure prediction to improve fragment library generation Bioinformatics, ():bty084
Mardia, K.V., Sriram, K. & Deane, C.M. (2018) A statistical model for helices with applications Biometrics, 74(3):845-854
Kovaltsuk, A., Krawczyk, K., Galson, J.D., Kelly, D.F., Deane, C.M. & Trück, J. (2017) How B-Cell Receptor Repertoire Sequencing Can Be Enriched with Structural Antibody Data Frontiers in Immunology, 8:1753
de Oliveira, S.H.P., Law, E.C., Shi, J. & Deane, C.M. (2017) Sequential search leads to faster, more efficient fragment-based de novo protein structure prediction Bioinformatics, 10
Knapp, B., Dunbar, J., Alcala, M. & Deane, C.M. (2017) Variable Regions of Antibodies and T-Cell Receptors May Not Be Sufficient in Molecular Simulations Investigating Binding Journal of Chemical Theory and Computation, 13(7):3097-3105
Pearce, N.M., Bradley, A.R., Krojer, T., Marsden, B.D., Deane, C.M. & von Delft, F. (2017) Partial-occupancy binders identified by the Pan-Dataset Density Analysis method offer new chemical opportunities and reveal cryptic binding sites Structural Dynamics, 4(3):32104
de Oliveira, S. & Deane, C. (2017) Co-evolution techniques are reshaping the way we do structural bioinformatics F1000Research, 6:1224
Marks, C., Nowak, J., Klostermann, S., Georges, G., Dunbar, J., Shi, J., Kelm, S. & Deane, C.M. (2017) Sphinx: merging knowledge-based and ab initio approaches to improve protein loop prediction Bioinformatics, 33(9):1346-1353
Marks, C. & Deane, C.M. (2017) Antibody H3 Structure Prediction Computional and Structural Biotechnology Journal, 15:222-231
Chen, J.W.C., Chen, Z.A., Rogala, K.B., Metz, J., Deane, C.M., Rappsilber, J. & Wakefield, J.G. (2017) Cross-linking mass spectrometry identifies new interfaces of Augmin required to localise the gamma-tubulin ring complex to the mitotic spindle. Biology open, 6(5):654-663
Deane, C.M. & Vásquez, M. (2017) Developability of Biotherapeutics: Computational Approaches . Edited by Sandeep Kumar and Satish K. Singh MAbs, 9(1):12-14
Demharter, S., Pearce, N., Beattie, K., Frost, I., Leem, J., Martin, A., Oppenheimer, R., Regep, C., Rukat, T., Skates, A., Trendel, N., Gavaghan, D.J., Deane, C.M. & Knapp, B. (2017) Ten simple rules for surviving an interdisciplinary PhD PLOS Computational Biology, 13(5):e1005512
Krawczyk, K., Demharter, S., Knapp, B., Deane, C.M. & Minary, P. (2017) In silico structural modeling of multiple epigenetic marks on DNA Bioinformatics
Luecken, M.D., Page, M.J.T., Crosby, A.J., Mason, S., Reinert, G. & Deane, C.M. (2017) CommWalker: Correctly Evaluating Modules in Molecular Networks in Light of Annotation Bias Bioinformatics
Leem, J., de Oliveira, S., Krawczyk, K. & Deane, C. (2017) STCRDab: the structural T-cell receptor database Nucleic Acids Research
Pearce, N.M., Krojer, T., Bradley, A.R., Collins, P., Nowak, R.P., Talon, R., Marsden, B.D., Kelm, S., Shi, J., Deane, C.M. & von Delft, F. (2017) A multi-crystal method for extracting obscured crystallographic states from conventionally uninterpretable electron density Nature Communications, 8:15123
Parks, T., Mirabel, M.M., Kado, J., Auckland, K., Nowak, J., Rautanen, A., Mentzer, A.J., Marijon, E., Jouven, X., Perman, M.L., Cua, T., Kauwe, J.K., Allen, J.B., Taylor, H., Robson, K.J., Deane, C.M., Steer, A.C. & Hill, A.V.S. (2017) Association between a common immunoglobulin heavy chain allele and rheumatic heart disease risk in Oceania Nature Communications, 8(14946)
Krawczyk, K., Dunbar, J. & Deane, C.M. (2017) Computational Tools for Aiding Rational Antibody Design Methods in Molecular Biology, 1529:399-416
de Oliveira, S.H.P., Shi, J. & Deane, C.M. (2017) Comparing co-evolution methods and their application to template-free protein structure prediction Bioinformatics, 33(3):373-381
Regep, C., Georges, G., Shi, J., Popovic, B. & Deane, C.M. (2017) The H3 loop of antibodies shows unique structural characteristics Proteins: Structure, Function, and Bioinformatics, 85(7):1311-1318
Dunbar, J., Krawczyk, K., Leem, J., Marks, C., Nowak, J., Regep, C., Georges, G., Kelm, S., Popovic, B. & Deane, C.M. (2016) SAbPred: a structure-based antibody prediction server. Nucleic Acids Research, 44(W1):W474-W478
Demharter, S., Knapp, B., Deane, C.M. & Minary, P. (2016) Modeling Functional Motions of Biological Systems by Customized Natural Moves Biophys. J., 111(4):710-721
Nowak, J., Baker, T., Georges, G., Kelm, S., Klostermann, S., Shi, J., Sridharan, S. & Deane, C.M. (2016) Length-independent structural similarities enrich the antibody CDR canonical class model. MAbs, 8(4):751-760
Ali, W., Wegner, A.E., Gaunt, R.E., Deane, C.M. & Reinert, G. (2016) Comparison of large networks with sub-sampling strategies Sci. Rep., 6:28955
Leem, J., Dunbar, J., Georges, G., Shi, J. & Deane, C.M. (2016) ABodyBuilder: automated antibody structure prediction with data-driven accuracy estimation MAbs, 8(7):1259-1268
Krawczyk, K., Sim, A.Y.L., Knapp, B., Deane, C.M. & Minary, P. (2016) Tertiary Element Interaction in HIV-1 TAR J. Chem. Inf. Model., 56(9):1746-1754
Law, E.C., Wilman, H.R., Kelm, S., Shi, J. & Deane, C.M. (2016) Examining the Conservation of Kinks in Alpha Helices PLoS One, 11(6):e0157553
Zhang, H., Lim, H.S., Knapp, B., Deane, C.M., Aleksic, M., Dushek, O. & van der Merwe, P.A. (2016) The contribution of major histocompatibility complex contacts to the affinity and kinetics of T cell receptor binding Sci. Rep., 6:35326
Wan, S., Knapp, B., Wright, D.W., Deane, C.M. & Coveney, P.V. (2015) Rapid, Precise, and Reproducible Prediction of Peptide.MHC Binding Affinities from Molecular Dynamics That Correlate Well with Experiment J. Chem. Theory Comput., 11(7):3346-3356
de Oliveira, S.H.P., Shi, J. & Deane, C.M. (2015) Building a Better Fragment Library for De Novo Protein Structure Prediction PLoS One, 10(4):e0123998
Alexander, L.T., Möbitz, H., Drueckes, P., Savitsky, P., Fedorov, O., Elkins, J.M., Deane, C.M., Cowan-Jacob, S.W. & Knapp, S. (2015) Type II Inhibitors Targeting CDK2. ACS Chem. Biol., 10(9):2116-2125
Knapp, B. & Deane, C.M. (2015) T-cell Receptor Binding Affects the Dynamics of the Peptide/MHC-I Complex. J. Chem. Inf. Model.
Knapp, B., Demharter, S., Deane, C.M. & Minary, P. (2015) Exploring peptide/MHC detachment processes using Hierarchical Natural Move Monte Carlo Bioinformatics, pages btv502
Dunbar, J. & Deane, C.M. (2015) ANARCI: Antigen receptor numbering and receptor classification Bioinformatics, pages btv552
Edwards, H. & Deane, C.M. (2015) Structural Bridges through Fold Space. PLoS Comput. Biol., 11(9):e1004466
Bujotzek, A., Dunbar, J., Lipsmeier, F., Schäfer, W., Antes, I., Deane, C.M. & Georges, G. (2015) Prediction of VH-VL domain orientation for antibody variable domain modeling. Proteins, 83(4):681-695
Knapp, B., Demharter, S., Esmaielbeiki, R. & Deane, C.M. (2015) Current status and future challenges in T-cell receptor/peptide/MHC molecular dynamics simulations Brief. Bioinform.
Knapp, B., Bardenet, R., Bernabeu, M.O., Bordas, R., Bruna, M., Calderhead, B., Cooper, J., Fletcher, A.G., Groen, D., Kuijper, B., Lewis, J., McInerny, G., Minssen, T., Osborne, J., Paulitschke, V., Pitt-Francis, J., Todoric, J., Yates, C.A., Gavaghan, D. & Deane, C.M. (2015) Ten Simple Rules for a Successful Cross-Disciplinary Collaboration. PLoS Comput. Biol., 11(4):e1004214
Esmaielbeiki, R., Krawczyk, K., Knapp, B., Nebel, J.C. & Deane, C.M. (2015) Progress and challenges in predicting protein interfaces Brief. Bioinform.
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